accurately measure time python function takes
Question:
I need to measure the time certain parts of my program take (not for debugging but as a feature in the output). Accuracy is important because the total time will be a fraction of a second.
I was going to use the time module when I came across timeit, which claims to avoid a number of common traps for measuring execution times. Unfortunately it has an awful interface, taking a string as input which it then eval’s.
So, do I need to use this module to measure time accurately, or will time suffice? And what are the pitfalls it refers to?
Thanks
Answers:
The timeit module looks like it’s designed for doing performance testing of algorithms, rather than as simple monitoring of an application. Your best option is probably to use the time module, call time.time() at the beginning and end of the segment you’re interested in, and subtract the two numbers. Be aware that the number you get may have many more decimal places than the actual resolution of the system timer.
According to the Python documentation, it has to do with the accuracy of the time function in different operating systems:
The default timer function is platform
dependent. On Windows, time.clock()
has microsecond granularity but
time.time()‘s granularity is 1/60th of
a second; on Unix, time.clock() has
1/100th of a second granularity and
time.time() is much more precise. On
either platform, the default timer
functions measure wall clock time, not
the CPU time. This means that other
processes running on the same computer
may interfere with the timing … On Unix, you can
use time.clock() to measure CPU time.
To pull directly from timeit.py‘s code:
if sys.platform == "win32":
# On Windows, the best timer is time.clock()
default_timer = time.clock
else:
# On most other platforms the best timer is time.time()
default_timer = time.time
In addition, it deals directly with setting up the runtime code for you. If you use time you have to do it yourself. This, of course saves you time
Timeit’s setup:
def inner(_it, _timer):
#Your setup code
%(setup)s
_t0 = _timer()
for _i in _it:
#The code you want to time
%(stmt)s
_t1 = _timer()
return _t1 - _t0
Python 3:
Since Python 3.3 you can use time.perf_counter() (system-wide timing) or time.process_time() (process-wide timing), just the way you used to use time.clock():
from time import process_time
t = process_time()
#do some stuff
elapsed_time = process_time() - t
The new function process_time will not include time elapsed during sleep.
Python 3.7+:
Since Python 3.7 you can also use process_time_ns() which is similar to process_time()but returns time in nanoseconds.
The documentation also mentions that time.clock() and time.time() have different resolution depending on platform. On Unix, time.clock() measures CPU time as opposed to wall clock time.
timeit also disables garbage collection when running the tests, which is probably not what you want for production code.
I find that time.time() suffices for most purposes.
Have you reviewed the functionality provided profile or cProfile?
http://docs.python.org/library/profile.html
This provides much more detailed information than just printing the time before and after a function call. Maybe worth a look…
You could build a timing context (see PEP 343) to measure blocks of code pretty easily.
from __future__ import with_statement
import time
class Timer(object):
def __enter__(self):
self.__start = time.time()
def __exit__(self, type, value, traceback):
# Error handling here
self.__finish = time.time()
def duration_in_seconds(self):
return self.__finish - self.__start
timer = Timer()
with timer:
# Whatever you want to measure goes here
time.sleep(2)
print timer.duration_in_seconds()
From Python 2.6 on timeit is not limited to input string anymore. Citing the documentation:
Changed in version 2.6: The stmt and setup parameters can now also take objects that are callable without arguments. This will embed calls to them in a timer function that will then be executed by timeit(). Note that the timing overhead is a little larger in this case because of the extra function calls.
I was annoyed too by the awful interface of timeit so i made a library for this, check it out its trivial to use
from pythonbenchmark import compare, measure
import time
a,b,c,d,e = 10,10,10,10,10
something = [a,b,c,d,e]
def myFunction(something):
time.sleep(0.4)
def myOptimizedFunction(something):
time.sleep(0.2)
# comparing test
compare(myFunction, myOptimizedFunction, 10, input)
# without input
compare(myFunction, myOptimizedFunction, 100)
I need to measure the time certain parts of my program take (not for debugging but as a feature in the output). Accuracy is important because the total time will be a fraction of a second.
I was going to use the time module when I came across timeit, which claims to avoid a number of common traps for measuring execution times. Unfortunately it has an awful interface, taking a string as input which it then eval’s.
So, do I need to use this module to measure time accurately, or will time suffice? And what are the pitfalls it refers to?
Thanks
The timeit module looks like it’s designed for doing performance testing of algorithms, rather than as simple monitoring of an application. Your best option is probably to use the time module, call time.time() at the beginning and end of the segment you’re interested in, and subtract the two numbers. Be aware that the number you get may have many more decimal places than the actual resolution of the system timer.
According to the Python documentation, it has to do with the accuracy of the time function in different operating systems:
The default timer function is platform
dependent. On Windows, time.clock()
has microsecond granularity but
time.time()‘s granularity is 1/60th of
a second; on Unix, time.clock() has
1/100th of a second granularity and
time.time() is much more precise. On
either platform, the default timer
functions measure wall clock time, not
the CPU time. This means that other
processes running on the same computer
may interfere with the timing … On Unix, you can
use time.clock() to measure CPU time.
To pull directly from timeit.py‘s code:
if sys.platform == "win32":
# On Windows, the best timer is time.clock()
default_timer = time.clock
else:
# On most other platforms the best timer is time.time()
default_timer = time.time
In addition, it deals directly with setting up the runtime code for you. If you use time you have to do it yourself. This, of course saves you time
Timeit’s setup:
def inner(_it, _timer):
#Your setup code
%(setup)s
_t0 = _timer()
for _i in _it:
#The code you want to time
%(stmt)s
_t1 = _timer()
return _t1 - _t0
Python 3:
Since Python 3.3 you can use time.perf_counter() (system-wide timing) or time.process_time() (process-wide timing), just the way you used to use time.clock():
from time import process_time
t = process_time()
#do some stuff
elapsed_time = process_time() - t
The new function process_time will not include time elapsed during sleep.
Python 3.7+:
Since Python 3.7 you can also use process_time_ns() which is similar to process_time()but returns time in nanoseconds.
The documentation also mentions that time.clock() and time.time() have different resolution depending on platform. On Unix, time.clock() measures CPU time as opposed to wall clock time.
timeit also disables garbage collection when running the tests, which is probably not what you want for production code.
I find that time.time() suffices for most purposes.
Have you reviewed the functionality provided profile or cProfile?
http://docs.python.org/library/profile.html
This provides much more detailed information than just printing the time before and after a function call. Maybe worth a look…
You could build a timing context (see PEP 343) to measure blocks of code pretty easily.
from __future__ import with_statement
import time
class Timer(object):
def __enter__(self):
self.__start = time.time()
def __exit__(self, type, value, traceback):
# Error handling here
self.__finish = time.time()
def duration_in_seconds(self):
return self.__finish - self.__start
timer = Timer()
with timer:
# Whatever you want to measure goes here
time.sleep(2)
print timer.duration_in_seconds()
From Python 2.6 on timeit is not limited to input string anymore. Citing the documentation:
Changed in version 2.6: The stmt and setup parameters can now also take objects that are callable without arguments. This will embed calls to them in a timer function that will then be executed by timeit(). Note that the timing overhead is a little larger in this case because of the extra function calls.
I was annoyed too by the awful interface of timeit so i made a library for this, check it out its trivial to use
from pythonbenchmark import compare, measure
import time
a,b,c,d,e = 10,10,10,10,10
something = [a,b,c,d,e]
def myFunction(something):
time.sleep(0.4)
def myOptimizedFunction(something):
time.sleep(0.2)
# comparing test
compare(myFunction, myOptimizedFunction, 10, input)
# without input
compare(myFunction, myOptimizedFunction, 100)